Object categorization based on a supervised mean shift algorithm

Du, R; Wu, Q; He, X; Yang, J

Object categorization based on a supervised mean shift algorithm

Du, R Wu, Q

He, X

Yang, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7585 LNCS (PART 3), pp. 611 - 614
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Du, R	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7585 LNCS (PART 3), pp. 611 - 614	en_US
dc.identifier.isbn	9783642338847	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31528
dc.description.abstract	In this work, we present a C++ implementation of object categorization with the bag-of-word (BoW) framework. Unlike typical BoW models which consider the whole area of an image as the region of interest (ROI) for visual codebook generation, our implementation only considers the regions of target objects as ROIs and the unrelated backgrounds will be excluded for generating codebook. This is achieved by a supervised mean shift algorithm. Our work is on the benchmark SIVAL dataset and utilizes a Maximum Margin Supervised Topic Model for classification. The final performance of our work is quite encouraging. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-33885-4_64	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Object categorization based on a supervised mean shift algorithm	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	7585 LNCS	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Florence, Italy
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7585 LNCS	en_US

Abstract:

In this work, we present a C++ implementation of object categorization with the bag-of-word (BoW) framework. Unlike typical BoW models which consider the whole area of an image as the region of interest (ROI) for visual codebook generation, our implementation only considers the regions of target objects as ROIs and the unrelated backgrounds will be excluded for generating codebook. This is achieved by a supervised mean shift algorithm. Our work is on the benchmark SIVAL dataset and utilizes a Maximum Margin Supervised Topic Model for classification. The final performance of our work is quite encouraging. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/31528